Overview

Dataset statistics

Number of variables26
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.3 KiB
Average record size in memory256.5 B

Variable types

Numeric15
Categorical11

Alerts

symboling is highly overall correlated with wheelbase and 2 other fieldsHigh correlation
wheelbase is highly overall correlated with symboling and 12 other fieldsHigh correlation
carlength is highly overall correlated with wheelbase and 10 other fieldsHigh correlation
carwidth is highly overall correlated with wheelbase and 11 other fieldsHigh correlation
carheight is highly overall correlated with symboling and 3 other fieldsHigh correlation
curbweight is highly overall correlated with wheelbase and 9 other fieldsHigh correlation
enginesize is highly overall correlated with wheelbase and 13 other fieldsHigh correlation
boreratio is highly overall correlated with wheelbase and 10 other fieldsHigh correlation
stroke is highly overall correlated with CarBrand and 1 other fieldsHigh correlation
compressionratio is highly overall correlated with fueltype and 3 other fieldsHigh correlation
horsepower is highly overall correlated with wheelbase and 12 other fieldsHigh correlation
peakrpm is highly overall correlated with fueltypeHigh correlation
citympg is highly overall correlated with carlength and 8 other fieldsHigh correlation
highwaympg is highly overall correlated with wheelbase and 10 other fieldsHigh correlation
price is highly overall correlated with wheelbase and 9 other fieldsHigh correlation
CarBrand is highly overall correlated with wheelbase and 10 other fieldsHigh correlation
fueltype is highly overall correlated with compressionratio and 2 other fieldsHigh correlation
aspiration is highly overall correlated with compressionratio and 1 other fieldsHigh correlation
doornumber is highly overall correlated with symboling and 2 other fieldsHigh correlation
carbody is highly overall correlated with doornumberHigh correlation
drivewheel is highly overall correlated with CarBrand and 1 other fieldsHigh correlation
enginelocation is highly overall correlated with wheelbase and 4 other fieldsHigh correlation
enginetype is highly overall correlated with enginesize and 3 other fieldsHigh correlation
cylindernumber is highly overall correlated with carwidth and 7 other fieldsHigh correlation
fuelsystem is highly overall correlated with compressionratio and 4 other fieldsHigh correlation
Classification is highly overall correlated with wheelbase and 13 other fieldsHigh correlation
fueltype is highly imbalanced (53.9%)Imbalance
enginelocation is highly imbalanced (89.0%)Imbalance
cylindernumber is highly imbalanced (57.6%)Imbalance
symboling has 67 (32.7%) zerosZeros

Reproduction

Analysis started2023-06-08 21:42:14.074924
Analysis finished2023-06-08 21:42:43.418561
Duration29.34 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size11.3 KiB
2023-06-08T16:42:43.591411image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2023-06-08T16:42:43.685967image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%

CarBrand
Categorical

Distinct22
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
112 

Length

Max length11
Median length10
Mean length6.2195122
Min length3

Characters and Unicode

Total characters1275
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Length

2023-06-08T16:42:43.780255image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Most occurring characters

ValueCountFrequency (%)
a 154
12.1%
o 152
 
11.9%
s 101
 
7.9%
t 100
 
7.8%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
4.9%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.8%
Other values (15) 381
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1272
99.8%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 154
12.1%
o 152
11.9%
s 101
 
7.9%
t 100
 
7.9%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.9%
Other values (14) 378
29.7%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1272
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 154
12.1%
o 152
11.9%
s 101
 
7.9%
t 100
 
7.9%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.9%
Other values (14) 378
29.7%
Common
ValueCountFrequency (%)
- 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 154
12.1%
o 152
 
11.9%
s 101
 
7.9%
t 100
 
7.8%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
4.9%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.8%
Other values (15) 381
29.9%

fueltype
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
gas
185 
diesel
20 

Length

Max length6
Median length3
Mean length3.2926829
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Length

2023-06-08T16:42:43.896861image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:44.017333image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 675
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

aspiration
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
std
168 
turbo
37 

Length

Max length5
Median length3
Mean length3.3609756
Min length3

Characters and Unicode

Total characters689
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Length

2023-06-08T16:42:44.131819image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:44.246753image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 689
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 689
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

doornumber
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
four
115 
two
90 

Length

Max length4
Median length4
Mean length3.5609756
Min length3

Characters and Unicode

Total characters730
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 115
56.1%
two 90
43.9%

Length

2023-06-08T16:42:44.349149image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:44.470636image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
four 115
56.1%
two 90
43.9%

Most occurring characters

ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 730
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 730
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

carbody
Categorical

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
sedan
96 
hatchback
70 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6195122
Min length5

Characters and Unicode

Total characters1357
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Length

2023-06-08T16:42:44.603505image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:44.829896image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1357
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

drivewheel
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
fwd
120 
rwd
76 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters615
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Length

2023-06-08T16:42:44.924994image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:45.020431image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 606
98.5%
Decimal Number 9
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 205
33.8%
d 205
33.8%
f 120
19.8%
r 76
 
12.5%
Decimal Number
ValueCountFrequency (%)
4 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 606
98.5%
Common 9
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 205
33.8%
d 205
33.8%
f 120
19.8%
r 76
 
12.5%
Common
ValueCountFrequency (%)
4 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

enginelocation
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
front
202 
rear
 
3

Length

Max length5
Median length5
Mean length4.9853659
Min length4

Characters and Unicode

Total characters1022
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Length

2023-06-08T16:42:45.106442image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:45.196280image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1022
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1022
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

wheelbase
Real number (ℝ)

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:45.713477image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2023-06-08T16:42:45.823279image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.2%
93.7 20
 
9.8%
95.7 13
 
6.3%
96.5 8
 
3.9%
97.3 7
 
3.4%
98.4 7
 
3.4%
104.3 6
 
2.9%
100.4 6
 
2.9%
107.9 6
 
2.9%
98.8 6
 
2.9%
Other values (43) 105
51.2%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.4%
93.3 1
 
0.5%
93.7 20
9.8%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.4%
108 1
 
0.5%
107.9 6
2.9%
106.7 1
 
0.5%

carlength
Real number (ℝ)

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:45.947131image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2023-06-08T16:42:46.069439image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.3%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
165.3 6
 
2.9%
177.8 6
 
2.9%
176.2 6
 
2.9%
186.6 6
 
2.9%
172 5
 
2.4%
Other values (65) 129
62.9%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.3%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

carwidth
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:46.183055image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2023-06-08T16:42:46.293228image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.8 24
 
11.7%
66.5 23
 
11.2%
65.4 15
 
7.3%
63.6 11
 
5.4%
64.4 10
 
4.9%
68.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
64.2 6
 
2.9%
Other values (34) 82
40.0%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.4%
63.8 24
11.7%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 2
 
1.0%
64.2 6
 
2.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

carheight
Real number (ℝ)

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:46.403310image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2023-06-08T16:42:46.513341image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.8%
52 12
 
5.9%
55.7 12
 
5.9%
54.1 10
 
4.9%
54.5 10
 
4.9%
55.5 9
 
4.4%
56.7 8
 
3.9%
54.3 8
 
3.9%
52.6 7
 
3.4%
56.1 7
 
3.4%
Other values (39) 108
52.7%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
2.9%
50.5 2
 
1.0%
50.6 5
 
2.4%
50.8 14
6.8%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

curbweight
Real number (ℝ)

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:46.623424image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2023-06-08T16:42:46.748480image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2410 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (161) 180
87.8%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

enginetype
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
ohc
148 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1268293
Min length1

Characters and Unicode

Total characters641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2023-06-08T16:42:46.842880image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:46.969543image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 641
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 641
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

cylindernumber
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
four
159 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.902439
Min length3

Characters and Unicode

Total characters800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-06-08T16:42:47.074311image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:47.201404image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 800
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

enginesize
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:47.313986image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2023-06-08T16:42:47.434172image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.3%
92 15
 
7.3%
97 14
 
6.8%
98 14
 
6.8%
108 13
 
6.3%
90 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.4%
92 15
7.3%
97 14
6.8%
98 14
6.8%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
2.9%

fuelsystem
Categorical

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
mpfi
94 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.897561
Min length3

Characters and Unicode

Total characters799
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-06-08T16:42:47.538331image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:47.725091image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 719
90.0%
Decimal Number 80
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 719
90.0%
Common 80
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

boreratio
Real number (ℝ)

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3297561
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:47.834897image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27084371
Coefficient of variation (CV)0.081340404
Kurtosis-0.78504183
Mean3.3297561
Median Absolute Deviation (MAD)0.26
Skewness0.020156418
Sum682.6
Variance0.073356313
MonotonicityNot monotonic
2023-06-08T16:42:47.929750image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.43 8
 
3.9%
3.78 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 83
40.5%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.4%
3.03 12
5.9%
3.05 6
2.9%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.4%
3.63 2
 
1.0%
3.62 23
11.2%
3.61 1
 
0.5%
3.6 1
 
0.5%

stroke
Real number (ℝ)

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2554146
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:48.047606image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31359701
Coefficient of variation (CV)0.096330898
Kurtosis2.1743964
Mean3.2554146
Median Absolute Deviation (MAD)0.14
Skewness-0.68970458
Sum667.36
Variance0.098343087
MonotonicityNot monotonic
2023-06-08T16:42:48.131361image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.4 20
 
9.8%
3.23 14
 
6.8%
3.15 14
 
6.8%
3.03 14
 
6.8%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.11 6
 
2.9%
Other values (27) 87
42.4%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.4%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.8%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.4%
3.58 6
2.9%
3.54 4
2.0%
3.52 5
2.4%
3.5 6
2.9%
3.47 4
2.0%
3.46 8
3.9%

compressionratio
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:48.236575image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2023-06-08T16:42:48.330416image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

horsepower
Real number (ℝ)

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.11707
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:48.432324image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.544167
Coefficient of variation (CV)0.37980483
Kurtosis2.6840062
Mean104.11707
Median Absolute Deviation (MAD)25
Skewness1.4053102
Sum21344
Variance1563.7411
MonotonicityNot monotonic
2023-06-08T16:42:48.542298image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
2.9%
160 6
 
2.9%
101 6
 
2.9%
62 6
 
2.9%
Other values (49) 117
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

peakrpm
Real number (ℝ)

Distinct23
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.122
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:48.636592image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation476.98564
Coefficient of variation (CV)0.093068155
Kurtosis0.086755856
Mean5125.122
Median Absolute Deviation (MAD)300
Skewness0.075158722
Sum1050650
Variance227515.3
MonotonicityNot monotonic
2023-06-08T16:42:48.730739image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.4%
Other values (13) 34
16.6%
ValueCountFrequency (%)
4150 5
 
2.4%
4200 5
 
2.4%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.6%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.0%
5400 13
 
6.3%
5300 1
 
0.5%
5250 7
 
3.4%

citympg
Real number (ℝ)

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:48.809286image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2023-06-08T16:42:48.903588image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.7%
19 27
13.2%
24 22
10.7%
27 14
 
6.8%
17 13
 
6.3%
26 12
 
5.9%
23 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.3%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.4%
37 6
2.9%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highwaympg
Real number (ℝ)

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:49.013431image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2023-06-08T16:42:49.107692image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
34 14
 
6.8%
37 13
 
6.3%
28 13
 
6.3%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.3%
25 19
9.3%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.3%

price
Real number (ℝ)

Distinct189
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13276.711
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-06-08T16:42:49.217753image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316503
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8715

Descriptive statistics

Standard deviation7988.8523
Coefficient of variation (CV)0.60171925
Kurtosis3.0516479
Mean13276.711
Median Absolute Deviation (MAD)3306
Skewness1.7776782
Sum2721725.7
Variance63821762
MonotonicityNot monotonic
2023-06-08T16:42:49.327668image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
9279 2
 
1.0%
7898 2
 
1.0%
8916.5 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (179) 185
90.2%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Classification
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
Expensive
103 
Cheap
102 

Length

Max length9
Median length9
Mean length7.0097561
Min length5

Characters and Unicode

Total characters1437
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExpensive
2nd rowExpensive
3rd rowExpensive
4th rowExpensive
5th rowExpensive

Common Values

ValueCountFrequency (%)
Expensive 103
50.2%
Cheap 102
49.8%

Length

2023-06-08T16:42:49.437605image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T16:42:49.531952image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
expensive 103
50.2%
cheap 102
49.8%

Most occurring characters

ValueCountFrequency (%)
e 308
21.4%
p 205
14.3%
E 103
 
7.2%
x 103
 
7.2%
n 103
 
7.2%
s 103
 
7.2%
i 103
 
7.2%
v 103
 
7.2%
C 102
 
7.1%
h 102
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1232
85.7%
Uppercase Letter 205
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 308
25.0%
p 205
16.6%
x 103
 
8.4%
n 103
 
8.4%
s 103
 
8.4%
i 103
 
8.4%
v 103
 
8.4%
h 102
 
8.3%
a 102
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
E 103
50.2%
C 102
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1437
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 308
21.4%
p 205
14.3%
E 103
 
7.2%
x 103
 
7.2%
n 103
 
7.2%
s 103
 
7.2%
i 103
 
7.2%
v 103
 
7.2%
C 102
 
7.1%
h 102
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 308
21.4%
p 205
14.3%
E 103
 
7.2%
x 103
 
7.2%
n 103
 
7.2%
s 103
 
7.2%
i 103
 
7.2%
v 103
 
7.2%
C 102
 
7.1%
h 102
 
7.1%

Interactions

2023-06-08T16:42:41.244883image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:17.770312image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:19.773805image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:21.553506image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:23.484786image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.204942image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.699678image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.149815image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:29.614190image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.153087image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.654374image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.444963image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.908746image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.244684image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.708255image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:41.359491image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:17.856558image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:19.873663image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:21.659146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:23.591062image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.298764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.795682image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.243966image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:29.708359image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.246760image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.732507image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.537631image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.003187image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.333368image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.810955image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:41.474914image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:17.950674image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:19.979308image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:21.783514image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:23.724614image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.408621image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.883963image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.340783image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:29.818279image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.333664image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.842575image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.639726image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.097487image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.440296image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.916814image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:41.605781image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.045559image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:20.101780image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:21.914236image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:23.856939image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.502956image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.993723image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.441097image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:29.912394image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.458059image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.961275image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.733667image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.176267image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.538009image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.039162image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:41.716694image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.138580image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:20.268062image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:22.034378image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:24.038320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.612536image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.087977image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.526322image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.022183image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.585423image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:34.351347image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.833781image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.269580image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.617880image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.158346image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:41.836313image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.232837image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:20.378994image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:22.155720image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:24.163948image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.706997image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.188668image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.620588image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.116526image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.699723image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:34.446237image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.942584image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.363884image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.712358image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.260169image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:41.959979image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.318839image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:20.494647image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:22.262200image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:24.293398image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.785634image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.264522image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.714807image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.210876image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.795982image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:34.549784image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.041543image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.442119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.819844image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.377342image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:42.054147image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.410034image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:20.621886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:22.392382image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:24.426886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.879723image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.358766image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.803208image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.304925image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.889647image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:34.632694image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.122072image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.534634image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.921845image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.465382image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:42.148382image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.497727image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:20.731924image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:22.562749image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:24.534943image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.989593image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.467370image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.915593image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.414778image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.992416image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:34.744039image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.216233image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.631460image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.027081image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.560242image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:42.242129image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.591445image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:20.845152image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:22.669308image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:24.641923image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.083771image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.553074image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.990219image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.493328image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.062911image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:34.833527image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.329043image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.710248image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.116476image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.656864image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:42.354061image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.688976image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:20.985693image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:22.786824image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:24.740415image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.178005image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.662260image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:29.100089image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.603180image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.173032image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:34.912342image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.466609image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.789014image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.213461image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.746114image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:42.462286image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.771106image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:21.105057image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:22.964379image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:24.830850image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.284292image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.767278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:29.178963image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.697407image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.264303image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.006217image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.554515image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.883368image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.295118image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.840525image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:42.566707image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:18.855020image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:21.215560image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:23.065757image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:24.915930image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.385589image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.857319image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:29.257407image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.791575image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.349744image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.111050image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.648772image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:37.962106image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.380293image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:40.919230image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:42.679169image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:19.562896image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:21.329974image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:23.214668image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.000155image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.487915image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:27.946136image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:29.367270image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:30.898446image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.450102image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.226839image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.742640image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.057064image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.472836image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:41.029074image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:42.788667image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:19.673187image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:21.415966image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:23.356370image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:25.095170image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:26.583086image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:28.055599image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:29.465795image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:31.016812image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:32.550012image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:35.326847image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:36.830061image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:38.150284image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:39.591367image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-06-08T16:42:41.124827image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-06-08T16:42:49.641764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
symbolingwheelbasecarlengthcarwidthcarheightcurbweightenginesizeboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgpriceCarBrandfueltypeaspirationdoornumbercarbodydrivewheelenginelocationenginetypecylindernumberfuelsystemClassification
symboling1.000-0.538-0.396-0.254-0.523-0.256-0.177-0.170-0.0190.023-0.0100.282-0.0180.053-0.1450.4430.2170.1850.6840.3340.2660.2720.2220.1600.2660.463
wheelbase-0.5381.0000.9120.8120.6330.7650.6480.5370.227-0.1260.505-0.312-0.493-0.5390.6820.5070.3410.3100.4450.3340.4170.5680.3530.3160.2260.598
carlength-0.3960.9121.0000.8880.5250.8900.7830.6390.187-0.1930.661-0.269-0.670-0.6980.8040.5000.1100.2070.3650.2410.4090.0000.3170.3560.3260.720
carwidth-0.2540.8120.8881.0000.3500.8640.7710.6100.240-0.1460.689-0.199-0.688-0.7010.8110.5270.2330.3010.3050.1280.4030.1600.3690.5670.2460.676
carheight-0.5230.6330.5250.3501.0000.3460.2000.216-0.0180.0000.011-0.296-0.069-0.1330.2430.4800.2770.2370.5410.4970.3600.2720.3880.3500.2920.429
curbweight-0.2560.7650.8900.8640.3461.0000.8780.7020.163-0.2190.808-0.236-0.813-0.8340.9090.4940.3050.3750.2740.2300.4560.1000.3270.4820.2920.779
enginesize-0.1770.6480.7830.7710.2000.8781.0000.7010.292-0.2350.817-0.273-0.730-0.7210.8260.5330.1570.2710.2070.2020.4690.6190.5270.6420.3330.700
boreratio-0.1700.5370.6390.6100.2160.7020.7011.000-0.083-0.1600.639-0.298-0.609-0.6150.6440.5330.1680.3350.1630.1510.4340.3270.4180.2580.3450.608
stroke-0.0190.2270.1870.240-0.0180.1630.292-0.0831.000-0.0700.130-0.074-0.030-0.0300.1110.5810.3750.2650.1320.1510.3380.6150.4040.2390.3030.257
compressionratio0.023-0.126-0.193-0.1460.000-0.219-0.235-0.160-0.0701.000-0.353-0.0220.4790.445-0.1740.4930.9930.5540.1860.0480.1140.0000.3380.5210.5180.259
horsepower-0.0100.5050.6610.6890.0110.8080.8170.6390.130-0.3531.0000.113-0.911-0.8860.8550.4570.2190.3430.1710.1890.4020.8430.5140.5640.3170.700
peakrpm0.282-0.312-0.269-0.199-0.296-0.236-0.273-0.298-0.074-0.0220.1131.000-0.131-0.057-0.0660.4700.5940.3110.2440.0740.2420.4480.3590.2830.3630.237
citympg-0.018-0.493-0.670-0.688-0.069-0.813-0.730-0.609-0.0300.479-0.911-0.1311.0000.968-0.8290.3600.3890.1860.0030.0000.3800.1100.2090.4240.3040.712
highwaympg0.053-0.539-0.698-0.701-0.133-0.834-0.721-0.615-0.0300.445-0.886-0.0570.9681.000-0.8230.4040.3360.3190.1190.0000.4370.1010.3250.5000.3410.726
price-0.1450.6820.8040.8110.2430.9090.8260.6440.111-0.1740.855-0.066-0.829-0.8231.0000.3810.3380.4070.0000.2290.4510.4510.2880.4290.2900.863
CarBrand0.4430.5070.5000.5270.4800.4940.5330.5330.5810.4930.4570.4700.3600.4040.3811.0000.3700.4100.2980.3170.6030.7030.6290.5440.5100.617
fueltype0.2170.3410.1100.2330.2770.3050.1570.1680.3750.9930.2190.5940.3890.3360.3380.3701.0000.3740.1610.1730.0880.0000.2500.1550.9850.090
aspiration0.1850.3100.2070.3010.2370.3750.2710.3350.2650.5540.3430.3110.1860.3190.4070.4100.3741.0000.0000.0000.1180.0000.1500.1960.6100.268
doornumber0.6840.4450.3650.3050.5410.2740.2070.1630.1320.1860.1710.2440.0030.1190.0000.2980.1610.0001.0000.7410.0500.0670.2000.1340.2450.000
carbody0.3340.3340.2410.1280.4970.2300.2020.1510.1510.0480.1890.0740.0000.0000.2290.3170.1730.0000.7411.0000.2140.4380.1320.0680.1440.183
drivewheel0.2660.4170.4090.4030.3600.4560.4690.4340.3380.1140.4020.2420.3800.4370.4510.6030.0880.1180.0500.2141.0000.1240.4250.3360.3870.600
enginelocation0.2720.5680.0000.1600.2720.1000.6190.3270.6150.0000.8430.4480.1100.1010.4510.7030.0000.0000.0670.4380.1241.0000.3990.2880.0000.040
enginetype0.2220.3530.3170.3690.3880.3270.5270.4180.4040.3380.5140.3590.2090.3250.2880.6290.2500.1500.2000.1320.4250.3991.0000.5460.3770.405
cylindernumber0.1600.3160.3560.5670.3500.4820.6420.2580.2390.5210.5640.2830.4240.5000.4290.5440.1550.1960.1340.0680.3360.2880.5461.0000.3730.503
fuelsystem0.2660.2260.3260.2460.2920.2920.3330.3450.3030.5180.3170.3630.3040.3410.2900.5100.9850.6100.2450.1440.3870.0000.3770.3731.0000.697
Classification0.4630.5980.7200.6760.4290.7790.7000.6080.2570.2590.7000.2370.7120.7260.8630.6170.0900.2680.0000.1830.6000.0400.4050.5030.6971.000

Missing values

2023-06-08T16:42:42.973139image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-08T16:42:43.277232image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symbolingCarBrandfueltypeaspirationdoornumbercarbodydrivewheelenginelocationwheelbasecarlengthcarwidthcarheightcurbweightenginetypecylindernumberenginesizefuelsystemboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgpriceClassification
03alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495.000Expensive
13alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500.000Expensive
21alfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500.000Expensive
32audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950.000Expensive
42audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450.000Expensive
52audigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250.000Expensive
61audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710.000Expensive
71audigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920.000Expensive
81audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875.000Expensive
90audigasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.01605500162217859.167Expensive
symbolingCarBrandfueltypeaspirationdoornumbercarbodydrivewheelenginelocationwheelbasecarlengthcarwidthcarheightcurbweightenginetypecylindernumberenginesizefuelsystemboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgpriceClassification
195-1volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415.0Expensive
196-2volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985.0Expensive
197-1volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515.0Expensive
198-2volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420.0Expensive
199-1volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950.0Expensive
200-1volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845.0Expensive
201-1volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045.0Expensive
202-1volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485.0Expensive
203-1volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470.0Expensive
204-1volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625.0Expensive